基于深度学习的胸部X射线扫描肺结节的自动检测:与背景肺部状况,结节特征和位置的关系
Midori Ueno1, Kotaro Yoshida2, Atsushi Takamatsu2
1Department of Radiology, Kanazawa University Graduate School of Medical Science, 1-13 Takaramachi, Kanazawa City, Ishikawa Prefecture 920-8641, Japan; Department of Radiology, University of Occupational and Environmental Health School of Medicine, 1-1 Iseigaoka, Kitakyushu City, Fukuoka Prefecture 807-8555, Japan.
European journal of radiology
|July 27, 2023
概括
基于深度学习的自动检测 (DLAD) 软件显示0.689的灵敏度和0.168假阳性率用于检测胸部X射线上的肺结节. 它的性能受到结节特征和重叠结构的影响.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 计算机辅助诊断 (CAD) 系统越来越多地用于帮助胸部放射图解读.
- 在现实世界中对CAD的评估,特别是基于深度学习的自动检测 (DLAD) 软件,是有限的.
- 了解在背景肺部异常的各种临床场景中DLAD的表现至关重要.
研究的目的:
- 评估基于深度学习的商业自动检测 (DLAD) 软件用于肺结节检测的独立性能.
- 在临床队列中评估软件的准确性,这些临床队列具有共同的背景肺部异常.
- 确定影响DLAD软件检测能力的因素.
主要方法:
- 对388名怀疑患有肺癌的患者进行了回顾性分析.
- 胸部放射图用DLAD软件进行分析,CT扫描作为结节识别的参考标准.
- 手动评估了结节的可见性,特征,位置,重叠结构,肺部疾病和背景肺部得分.
主要成果:
- 在手动识别的322个结节中,DLAD软件检测到222个,达到0.689的灵敏度和0.168.168的错误阳性率.
- 对于小的,亚固体结节,以及那些被重叠结构所掩盖的结节,检测灵敏度明显较低.
- 结节可见度得分和DLAD检测灵敏度之间观察到正相关性;背景肺得分的影响是不确定的.
结论:
- 肺结节检测DLAD软件的独立性能产生了0.689的灵敏度和0.168.168的错误阳性率.
- 了解DLAD软件的特定性能特征和局限性对于准确解释胸部X射线图至关重要.
- 在复杂的临床病例中,可能需要进一步的研究来优化DLAD性能.
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